Job Stress and Job Satisfaction: Home Care Workers in a Consumer‐Directed Model of Care
Bibliographic record
Abstract
OBJECTIVE: To investigate determinants of job satisfaction among home care workers in a consumer-directed model. DATA SOURCES/SETTING: Analysis of data collected from telephone interviews with 1,614 Los Angeles home care workers on the state payroll in 2003. DATA COLLECTION AND ANALYSIS: Multivariate logistic regression analysis was used to determine the odds of job satisfaction using job stress model domains of demands, control, and support. PRINCIPAL FINDINGS: Abuse from consumers, unpaid overtime hours, and caring for more than one consumer as well as work-health demands predict less satisfaction. Some physical and emotional demands of the dyadic care relationship are unexpectedly associated with greater job satisfaction. Social support and control, indicated by job security and union involvement, have a direct positive effect on job satisfaction. CONCLUSIONS: Policies that enhance the relational component of care may improve workers' ability to transform the demands of their job into dignified and satisfying labor. Adequate benefits and sufficient authorized hours of care can minimize the stress of unpaid overtime work, caring for multiple consumers, job insecurity, and the financial constraints to seeking health care. Results have implications for the structure of consumer-directed models of care and efforts to retain long-term care workers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".